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International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 101–107Cite as

An Efficient Pedestrian Detector Based on Saliency and HOG Features Modeling

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

Abstract

Most of pedestrian detection existing approaches rely on applying descriptors to the entire image or use a sliding window which resize the matching window at different scales and scan the image. However, these methods suffer from low computational efficiency and time consuming. We propose in this paper the use of saliency detection based on contourlet transform to generate a region of interest (ROI). The resulting saliency map is then used for features extraction using the HOG descriptor. Finally, the distribution of the generated features is estimated by a two-parameter Weibull model. The built feature vector is after trained using a support vector regression (SVR) classifier. Thus, the proposed approach provides two contributions. (1) By designing a saliency detection, we aim to remove noisy and busy background and focus on the area where the object exists which enhance the accuracy of the classification process. (2) By modeling the generated features, we intend to reduce the training dimension and make the system computationally efficient in real-time, or soft real-time. The results of the experimental study made on the challenging INRIA data set prove the effectiveness of the proposed approach.

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Author information

Authors and Affiliations

  1. LRIT, Associated Unit to CNRST (URAC No 29), Faculty of Sciences, Mohammed V University, B.P. 1014 RP, Rabat, Morocco

    Mounir Errami & Mohammed Rziza

Authors
  1. Mounir Errami
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  2. Mohammed Rziza
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Corresponding author

Correspondence to Mounir Errami .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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Errami, M., Rziza, M. (2016). An Efficient Pedestrian Detector Based on Saliency and HOG Features Modeling. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_11

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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